use npu_moe_gating_top_k_softmax (#1355)

### What this PR does / why we need it?
The optimization solution for non-deepseek select_experts is to replace
gating_topk_softmax with softmax+topk+to, which is optimized from 37us
to 14us on bf16/fp16 of qwen3-235b

- vLLM version: v0.9.2
- vLLM main:
1a4f35e2ea

---------

Signed-off-by: ttanzhiqiang <389825161@qq.com>

tests/e2e/singlecard/ops/test_gating_top_k_softmax.py

@@ -0,0 +1,37 @@
+import pytest
+import torch
+import torch_npu
+
+
+@pytest.mark.parametrize(
+    'B',
+    [1, 16, 64, 128, 32768],
+)
+@pytest.mark.parametrize(
+    'D',
+    [8, 16, 32, 64, 128],
+)
+@pytest.mark.parametrize(
+    'top_k',
+    [1, 2, 4, 8],
+)
+@pytest.mark.parametrize(
+    "dtype, atol, rtol",
+    [
+        (torch.float16, 1e-3, 1e-3),
+        (torch.bfloat16, 1e-3, 1e-3),
+    ],
+)
+def test_quant_fpx_linear(B: int, D: int, top_k: int, dtype, atol, rtol):
+    x = torch.rand((B, D), dtype=dtype).to("npu")
+    # finished = torch.randint(1, size=(B,), dtype=torch.bool).to("npu")
+    finished = None
+    y, expert_idx, row_idx = torch_npu.npu_moe_gating_top_k_softmax(x,
+                                                                    finished,
+                                                                    k=top_k)
+
+    topk_weights = x.softmax(dim=-1)
+    topk_weights, topk_ids = topk_weights.topk(top_k, dim=-1)
+    topk_ids = topk_ids.to(torch.int32)
+    torch.allclose(y, topk_weights, atol=atol, rtol=rtol)
+    torch.allclose(expert_idx, topk_ids, atol=atol, rtol=rtol)